Query-by-Dancing: A Dance Music
Retrieval System Based on Body-Motion
Similarity
Shuhei Tsuchida(B) , Satoru Fukayama(B) , and Masataka Goto(B)
National Institute of Advanced Industrial Science and Technology (AIST),
Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki, Japan{s-tsuchida,s.fukayama,m.goto}@aist.go.jp

Abstract. This paper presents Query-by-Dancing, a dance music
retrieval system that enables a user to retrieve music using dance
motions. When dancers search for music to play when dancing, theysometimes ﬁnd it by referring to online dance videos in which the dancers
use motions similar to their own dance. However, previous music retrieval
systems could not support retrieval specialized for dancing because they
do not accept dance motions as a query. Therefore, we developed ourQuery-by-Dancing system, which uses a video of a dancer (user) as the
input query to search a database of dance videos. The query video isrecorded using an ordinary RGB camera that does not obtain depth
information, like a smartphone camera. The poses and motions in thequery are then analyzed and used to retrieve dance videos with similarposes and motions. The system then enables the user to browse the music
attached to the videos it retrieves so that the user can ﬁnd a piece that is
appropriate for their dancing. An interesting problem here is that a simple search for the most similar videos based on dance motions sometimes
includes results that do not match the intended dance genre. We solved
this by using a novel measure similar to tf-idf to weight the importance ofdance motions when retrieving videos. We conducted comparative experiments with 4 dance genres and conﬁrmed that the system gained anaverage of 3 or more evaluation points for 3 dance genres (waack, pop,
break) and that our proposed method was able to deal with diﬀerent
dance genres.
Keywords: Dance · Music · Video · Retrieval system · Body-motion

1

Introduction

Dancers often dance to music. They choose a dancing style that can match thegenre or style of a musical piece, synchronize their movements with musicalbeats and downbeats, and change their movements to follow musical changes.
When musical pieces are played on a dance performance stage, for example,
the dancers just have to dance to match the piece being performed. On thec Springer Nature Switzerland AG 2019

I. Kompatsiaris et al. (Eds.): MMM 2019, LNCS 11295, pp. 251–263, 2019.
https://doi.org/10.1007/978-3-030-05710-7_21

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other hand, when dancers can select musical pieces for their dance performances,
practices, or personal enjoyment, they spend a lot of time ﬁnding musical pieces
appropriate for their intended performance. This is because selecting pieces ofmusic is important for achieving successful dance performances and enjoying
dancing. When searching for music to play while dancing, dancers sometimes
ﬁnd it by referring to online dance videos in which the dancers use motions
similar to their own dance. They may also refer to dance events or showcasesof their favorite dancers or dance groups for music. Since there has been nosystematic support for ﬁnding certain kinds of dance music, such activities havebeen time consuming and diﬃcult. Although many music retrieval systems havebeen proposed [2, 7, 9], none have focused on retrieving dance music.
Therefore, we developed a dance music retrieval system called Query-byDancing that enables a dancer (user) to use his/her dance motions to retrieve
music. To ﬁnd music for the dancer to dance to, our system ﬁrst ﬁnds a dancemusic video that contains dancing similar to the motions of the user’s dance.
Our system can retrieve dance videos that include motions similar to an input
query of a short video capturing dancing body motions. The musical pieces inthe videos should be appropriate for the user to dance to.
Our Query-by-Dancing system does not need an expensive high-performance
motion capture system or a camera that obtains depth information. It only needs
a simple RGB camera like those installed in smartphones. We implemented asystem that analyzes input query videos and a database of dance videos by using
the OpenPose library by Cao et al. [1] to estimate body motions.

2

Related WorkA number of music retrieval and recommendation systems have been proposed,
but none have allowed a dancer to search for music using dance motions. Our
system, Query-by-Dancing, is equipped with a novel function based on the similarities between dance motions that enables the user to input their own dance
video as a retrieval query. We surveyed studies on music retrieval and recommendation systems using various queries.
Ghias et al. [4] proposed a query-by-humming system that uses humming asa query. They claim that an eﬀective and natural way of searching a musicalaudio database is by humming the song. Chen et al. [3] proposed a system for
retrieving songs from music databases using rhythm. They use strings of notes
as music information, and the database returns all songs containing patterns
similar to the query. Jang et al. [5] proposed a query-by-tapping system. The
system allows the user to search a music database by tapping on a microphone
to input the duration of the ﬁrst several notes of the query song. Maezawa et al.
[6] proposed a query-by-conducting system. In this system, the interface allows
a user to conduct during the playback of a piece, and the interface dynamicallyswitches the playback to a musical piece that is similar to the user’s conducting.
Some systems retrieve musical pieces by using a musical context, such as
the artist’s cultural or political background, collaborative semantic labels, andA Dance Music Retrieval System Based on Body-Motion Similarity

253

album cover artwork [10]. Turnbull et al. [11] presented a query-by-text systemthat can use a text-based query to retrieve relevant tracks from a database of
unlabeled audio content. This system can also annotate novel audio tracks withmeaningful words.
As described above, several retrieval methods using various queries have been
proposed. However, to the best of our knowledge, this is the ﬁrst study that has
used dance motions as a query for retrieving music. Our system focuses on dance
motions and acquires candidate musical pieces from a dance video database.

3

Dance Music Retrieval System

The system overview is shown in Fig. 1. Our system can be divided into twostages: pre-processing and similarity calculation. These two main stages aredescribed below.

Fig. 1. System overview.

3.1

Pre-processing

Detect a Dancer. In this step, the system ﬁrst estimates the person’s skeleton
information in all video frames using the OpenPose library [1]. Dance videos
sometimes include frames of multiple people dancing or the OpenPose librarysometimes detects skeleton information incorrectly on a frame that has no person. Therefore, the system selects the skeleton representing the main dancer by
analyzing all of the skeletons detected in each frame. First, the area (Ao ) occupied by each skeleton detected is computed by multiplying the width by theheight of the area. The width is deﬁned as the diﬀerence between the maximum
and minimum values along the x-axis direction of the detected skeleton(s). The
height is deﬁned as the diﬀerence between the maximum and minimum values
in the y-axis direction of the skeleton(s). Then, the position (Pd ) of a dancer

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in a video is obtained by averaging the skeleton positions in all of the frames.
The distance (Dc ) from the center (Pc (Xmean , Ymean )) of the entire frame image
to Pd is computed for each dancer. Assuming the main dancer is located in the
Aois selected as the main dancer.
center, the skeleton that maximizes R = D
cFeature Extraction. To calculate the similarity between dance motions in thequery video and each dance video in the database, we extracted the following
motion features from the skeleton of the main dancer. Since both poses andmotions are important elements that characterize dancing, the system ﬁrst represents the poses by calculating 17 joint angles from the skeleton per frame. The
angle is calculated clockwise from the upper side vertically across the image aszero. The joint angles are broken into the two dimensions θx and θy by calculating
sine and cosine (as shown in Fig. 2), and we denote a 34-dimensional feature vec(i)tor of angles at n-th frame of i-th video by vθ (n)(1 ≤ n ≤ N (i) and 1 ≤ i ≤ I),
where N is the number of frames in the i-th video and I is the number of videos
in the database. Furthermore, the angle where the skeleton was not detected isexpressed as zero.

Fig. 2. All the joint angles are broken down per frame, and creating a 34-dimensional
vector.

The system then represents the body motions by calculating the speed and(i)acceleration of the change in joint angles between frames. It calculates vΔθ (n)(i)
and vΔ2 θ (n) as follows:vΔθ (n) = abs(vθ (n) − vθ (n − 1))

(i)

(i)

(i)

(1)

(i)

(i)

(i)

(2)

vΔ2 θ (n) = abs(vΔθ (n) − vΔθ (n − 1))

where abs(x) denotes a vector containing the absolute value of each element of
(i)
(i)
(i)
x. We concatenated the above 3 feature vectors, vθ (n), vΔθ (n), and vΔ2 θ (n),into one 102-dimensional vector v (i) (n).

A Dance Music Retrieval System Based on Body-Motion Similarity

3.2

255

Similarity Calculation

The system calculates the Euclidean distances d(v in (n), v (i) (m)) between all
frames (1 ≤ n ≤ N in ) of an input video (in) and all frames (1 ≤ m ≤ N (i) ) of avideo in the video database (1 ≤ i ≤ I), where d(x, y) denotes an Euclidean distance between x and y, as shown in Fig. 3. The system computes these Euclidean
distances in all frame combinations and divides them by the total number of
combinations (N in N (i) ). They are denoted by following formula:
N in N (i)

R

(i)


1
= in (i)d(v in (n), v (i) (m)).
N N

(3)

Fig. 3. The system computes the Euclidean distances in all frame combinations anddivides them by the total number of combinations.

Now the system simply ﬁnds the most similar videos by using R(i) as thesimilarity of the dance motions per video, which leads to results that do notmatch the intended dance genre. We solved this problem by using a novel measure similar to tf-idf to weight the importance of dance motions when retrieving
videos. The weight representing the importance of dance motions is calculated
as follows:N (i)
1d(v in (n), v (i) (m))
N (i)
.
(4)
W (n) =
N (i)
max{ N1(i)d(v in (n), v (i) (m)}i∈Iwhere max(x) denotes a maximum value of x. To sharpen the weight gradient,
the system calculates W (n) to the 30-th power to obtain W  (n). We determinedthe exponent 30 experimentally. Then, the system multiplied W  (n) by all of the
Euclidean distances. These distances are determined with the following formula:
N in
U

(i)

=

(W  (n)N (i)d(v in (n), v (i) (m))).
N in N (i)

(5)

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The system ﬁnds the videos with the top k values among U (i) as the videos that
contain dance motions similar to those in the input video. Finally, the systempresents the candidate musical pieces from the dance videos searched.

4

Evaluation

We conducted two evaluation experiments to investigate whether the retrieval
results are easy for dancers to use as dance music. In the ﬁrst experiment, thesystem retrieved dance music based on whether the importance of each video
was weighted or not. We adapted the calculated weights to the two methods;
ADD method and DTW method, and retrieved dance music using each method.
In the second experiment, the system retrieved dance music by using the dance
videos of 4 dance genres as queries. This retrieval was done using the methodthat gave the best results in the ﬁrst experiment.
4.1

Dance Video Database

We used 100 dance videos available on YouTube and Instagram. They were 25
videos per each dance genre we chose—break, hip-hop, waack, and pop—and
their average duration was 82 s. The audio track of all these videos contained
music that the dancers in the videos danced to.
4.2

Experiment I: Weighted or Unweighted

Experiment Conditions. We recruited 12 participants (4 males and 8 females)
who were students belonging to a street dance club. All had between 1 to 15
years of dance experience (average = 8.5 years).
We compared 4 retrieval methods: ADD (unweighted), ADD (weighted),
DTW (unweighted), and DTW (weighted). The ADD method, our proposed
retrieval method using the Euclidean distance between frames, calculates one(i)(i)
feature vector v (i) (n) of 102 dimensions by concatenating vθ (n), vΔθ (n), and(i)vΔ2 θ (n) per frame. ADD (unweighted) does not use W  (n), and it lists musical pieces in ascending order of R(i) . ADD (weighted) uses W  (n), and it lists
musical pieces in ascending order of U (i) .
The DTW method is a retrieval method that uses dynamic time warping, asequence matching algorithm that considers longer-term similarity. This method(i)creates a sequence Vdtw (n)(1 ≤ n ≤ N (i) − 5 and 1 ≤ i ≤ I) for every 6 frames(i)by sliding vθ (n) one frame at a time. The system calculates the dynamic time(i)inwarping dtw(vdtw(n), Vdtw (m)) between all sequences (1 ≤ n ≤ N in − 5) of
an input video (in) and all sequences (1 ≤ m ≤ N (i) − 5) of a video in thevideo database (1 ≤ i ≤ I), where dtw(x, y) is the Euclidean distance between(i)x and y calculated by FastDTW [8]. Then, Rdtw and Wdtw (n) are calculatedusing an equation in which the d(v in (n), v (i) (m)) in Eq. (3) are replaced with(i)in(n), Vdtw (m)). To sharpen the weight gradient, the system calculates
dtw(vdtw

A Dance Music Retrieval System Based on Body-Motion Similarity257


Wdtw (n) to the 40-th power to obtain Wdtw(n). We determined the exponent(n) by all the Euclidean
40 experimentally. Then, the system multiplies Wdtw(i)
distances and obtains Udtw . DTW (unweighted) does not use Wdtw(n), and it(i)
lists musical pieces in ascending order of Rdtw . DTW (weighted) uses Wdtw(n)(i)
and lists musical pieces in ascending order of Udtw .
We asked a waack dancer with 15 years of dance experience to participate in
the experiment and shot about 11 s of her waack dancing. With that video as aquery, we used each of the 4 methods to retrieve musical pieces. We denoted the
top 5 music groups in the retrieval results obtained with each of the 4 methods
as MG-A, MG-B, MG-C, and MG-D. Each music group had 5 musical pieces.

Procedure. At the beginning of the session, participants ﬁlled out a pre-study
questionnaire about their dance experience. Then, we gave them a brief explanation of the experiment. After watching a query of the waack dancer’s 11-s video
without music, they were asked to listen to the 5 musical pieces in each musicgroup and evaluate them on a 5-point Likert scale ranging from 1 for “do notagree” to 5 for “totally agree.” They were given the music groups MG-A, MG-B,
MG-C, and MG-D in random order. We gave them the evaluation item below.
Q1: Given the assumption that “someone” dances with the choreography shownin this video while listening to music, is each of these 5 musical pieces easy
to dance to according to its atmosphere and the atmosphere of the choreography?
Finally, they ﬁlled out a questionnaire about the dance music retrieval.
We prepared a MacBook Pro (Retina display, 15-in., mid-2015) and used the
QuickTime Player to play the musical pieces and the video. The query dance
video was set to “repeat play” beforehand and the participant selected andplayed the 5 musical pieces arranged next to it. The participants wore earphones
to listen to the music and could play and re-evaluate the musical pieces as
many times as they wanted. The participants could take breaks freely during
the experiment. The experiment took about 40 min.
Results and Discussion. The averaged Q1 scores for each retrieval method
are shown in Fig. 4. The vertical axis indicates the average of the Q1 scores
given by all of the participants, and the vertical bars indicate standard errors.
The horizontal axis represents retrieval methods. The gray rectangles show theaveraged evaluation scores for each of the retrieval ranks. Each green rectangle shows the average of all evaluation scores within the retrieval method. We
assessed the diﬀerence between the average Q1 scores with ANOVA. There wasa signiﬁcant diﬀerence (F(3,236) = 4.21, p < .05). We also assessed the diﬀerencewith Fisher’s Least Signiﬁcant Diﬀerence (LSD) test and found signiﬁcant diﬀerences (p < .05) between ADD (weighted) and the other 3 methods. Thus, ADD(weighted) was the suitable retrieval method of searching for musical pieces thatdancers can easily dance to.

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Fig. 4. The averaged Q1 scores for each retrieval method. The ADD (weighted) method
was evaluated as signiﬁcantly higher than the other 3 methods.

The retrieval results of ADD (weighted) had the same dance genres as thequery more often than the other retrieval methods, which increased the evaluation score of ADD (weighted). The dance genres of each retrieval rank for each
retrieval method are shown in Table 1. Focusing on the top 5 musical pieces in
the retrieval results, we found that waack was 4 out of 5 musical pieces for ADD(weighted), which was the same dance genre as the query. For the other methods,
2 out of 5, 1 out of 5, and 3 out of 5 musical pieces were waack. The musicalpieces used in videos of the same genre as the query’s got higher evaluation
scores.
Table 1. Top 5 retrieval results by retrieval methods. P in the table stands for thedance genre pop, and W stands for the dance genre waack.
Retrieval method

Dance genre Retrieval rank
1 2 3 4 5

ADD (unweighted) Waack
ADD (weighted)
DTW (unweighted)
DTW (weighted)

W
W
P
P

P
P
P
W

W
W
W
W

P
W
P
W

P
W
P
P

Next, we focused on the weights. Figure 4 shows that the scores of theweighted methods were higher than the unweighted methods’, and that weighting is eﬀective for retrieving dance music appropriate for dance motions. The
calculated weight W  (n) is shown in Fig. 5, where the vertical axis indicates theweight value and the horizontal axis represents the frame numbers in the video
used as the query. The high-weight movements in the vicinity of frames 240 to
270 were movements such as the dancer swinging her arm above her head in long

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259

Fig. 5. Waack’s characteristic movements were in the vicinity of a relatively high weightvalue. The movements common to other dance genres were in the vicinity of a relatively
low weight value.

strides. Moreover, the movements in the vicinity of the ﬁrst 50 frames with a relatively high weight value were movements such as the dancer swinging her arm
to the left and right in long strokes. Swinging the arm in long strokes, a characteristic waack movement, had been highly weighted. On the other hand, themovements in the vicinity of frames 50 to 75 with a relatively low weight value
were simple movements like moving backwards. Moreover, the movements in thevicinity of frames 200 to 225 with a relatively low weight value were movements
such as the dancer shaking her waist to the left and right. These movements
also occur in other dance genres. As the above shows, the system successfully
weighted the movement particular to the dance motion in the query. In contrast,
movements common to other dance genres were weighted low.
4.3

Experiment II: Retrieval Performance

Experiment Conditions. We recruited 12 participants (6 males and 6 females)
who were students belonging to a street dance club. All participants had 1 to 15
years of dance experience (average = 5.9 years). We compared 4 dance genres:waack, break, pop, and hip-hop. We prepared the waack video used in the ﬁrst
experiment. The author who has 8 years of dance experience was in charge of abreakdancer, and we shot about 13 s of that author’s breakdancing. To prepare
other videos, we recruited two more dancers, a pop dancer and a hip-hop dancer.
The pop dancer had 3 years of dance experience, and we shot about 16 s of his
pop dance. The hip-hop dancer had 15 years of dance experience, and we shot
about 16 s of her hip-hop dance. Using those videos as queries, we retrieved
musical pieces by using the ADD (weighted) method. We denoted the top 5
music groups in the retrieval results obtained in each of the dance genres as
DG-W, DG-B, DG-H, and DG-P. Each music group had 5 musical pieces.

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Procedure. At the beginning of the session, participants ﬁlled out a pre-study
questionnaire about their dance experience. Then, we brieﬂy explained the experiment. After watching a query that was one of the randomly selected dance videos
without music, they were asked to listen to the 5 musical pieces in each musicgroup and evaluate them on a 5-point Likert scale ranging from 1 for “do notagree” to 5 for “totally agree.” They were given the music groups DG-W, DG-B,
DG-H, and DG-P according to the dance genre of the video. We gave Q1 as theevaluation item. Finally, they were orally interviewed.
We prepared a MacBook Pro (Retina display, 15-in., mid-2015) and used the
QuickTime Player to play the musical pieces and the video. The query dance
video was set to “repeat play” beforehand, and the participants selected andplayed the 5 musical pieces arranged next to it. The participants wore earphones
to listen to the music, and they could play and re-evaluate the musical pieces
as many times as they wanted. The participants could take breaks freely during
the experiment. The experiment took about 40 min.

Fig. 6. The averaged Q1 scores for each dance genre.

Results and Discussion. Figure 6 shows the averaged Q1 scores for each dance
genre. The vertical axis indicates the average of the Q1 scores from all of theparticipants, and the vertical bars indicate standard errors. The horizontal axis
represents dance genres. The gray rectangles indicate the averaged evaluation
scores for each retrieval rank. Each green rectangle indicates the average of
all evaluation scores within the genre. We assessed the diﬀerence between the
average Q1 scores with ANOVA. There was a signiﬁcant diﬀerence (F(3,236) =3.92, p < .05). We also assessed the diﬀerence with Fisher’s Least Signiﬁcant
Diﬀerence (LSD) test and found signiﬁcant diﬀerences (p < .05) between waack
and hip-hop, break and hip-hop, and break and pop.
Table 2 shows the Q1 evaluation scores and dance genres of each retrieval
rank for each retrieval method. The hip-hop video had a comparatively bad
performance as the query. The system returned musical pieces in the break genre.
There are two reasons for this. Hip-hop is divided into more dance subgenres. The

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261

Table 2. Top 5 retrieval results by dance genre. P in the table stands for pop, W for
waack, and B for break.
Retrieval method Dance genre Retrieval rank
1 2 3 4 5
ADD (weighted)

Waack
Break
Hip-hop
Pop

W
B
B
P

P
B
B
P

W
B
B
P

W
B
B
P

W
B
B
P

style of the hip-hop in our input video was middle hip-hop, but the hip-hop dancestyles in the database were style hip-hop, girls hip-hop, jazz hip-hop, etc., dancestyles slightly diﬀerent from the query. Therefore, it was hard for the system toextract the musical pieces of hip-hop. The other reason is that middle hip-hop has
some movements similar to those of break. One characteristic of break is when
the dancer continues to dance while keeping their hands on the ﬂoor. However,before keeping their hands on the ﬂoor, breakdancers’ movements are similar
to middle hip-hop. Therefore, the system selected musical pieces for break. On
the other hand, the system could extract motions similar to the query, which
prevented the evaluation scores from markedly decreasing. For these two reasons,the system extracted musical pieces for break that were not the same dance genreof the query. In the future, we will divide the dance genres even further and add
more dance genres into the database, which will improve evaluation scores.
The evaluation score for pop was worse than that of break and tended to beworse than that of waack. We interviewed the participants who gave a low score
to pop to determine the reason for this. They said they scored it thus because
the dance motions used as the query included a “vibration” technique in which
the dancers move their bodies with a rapid trembling motion and a “wave”
technique in which the dancers move their bodies like a wave. Those movements
match speciﬁc sounds, and the participants decided that musical pieces that
did not include those sounds were inappropriate for those movements. We cansolve this problem by using interactive retrieval methods that let dancers adjust
parameters according to their purposes. For example, if dancers want to searchfor speciﬁc musical pieces used in videos containing movements that closely
resemble particular movements (like “waves”), the system will allow the dancers
to search through a narrow range of musical pieces by adjusting parameters tomatch highly similar movements. In addition, if users want to search for musicalpieces to practice to or to dance to in a club with many other dancers, thesystem will allow the users to search through a large range of various musicalpieces by adjusting parameters to match the movements with a lower similarity.
Users changing the parameters contextually could realize more eﬃcient dancemusic retrieval.

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Conclusion

We proposed Query-by-Dancing, which is a dance music retrieval system thatenables a user to retrieve a musical piece using dance motions. We conﬁrmedthat the system’s retrieval method is appropriate for dance music, that the system can ﬁnd musical pieces that are easy to dance to, and that better music
can be obtained by weighting the importance of dance motions when retrieving videos. Moreover, we conducted comparative experiments on 4 dance genres
and conﬁrmed that the system scored an average of 3 points or more evaluation
points for 3 dance genres (waack, pop, and break), and our method can adapt
to diﬀerent dance genres. In the future, we plan to add a wider range of dance
genres to the database of dance videos.
Acknowledgments. This work was supported in part by JST ACCEL Grant Number
JPMJAC1602, Japan.

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